GPT-5 Chat vs Qwen3 235B-A22B Thinking 2507 vs Qwen MT Plus
Qwen3 235B-A22B Thinking 2507 comes out ahead, 42 to 31 and 11 on our weighted score, and it is the cheaper option too.
OpenAIGPT-5 Chat
31/100- ECI—
- Price$1.25 / $10.00
- Context400K
- Our pick
Alibaba (Qwen)Qwen3 235B-A22B Thinking 2507
42/100- ECI143.8
- Price$0.30 / $2.90
- Context262K
- Alibaba (Qwen)
Qwen MT Plus
11/100- ECI—
- Price$2.46 / $7.37
- Context16K
Qwen3 235B-A22B Thinking 2507 is our pick
Qwen3 235B-A22B Thinking 2507 is the better all-round choice, scoring 42/100 against GPT-5 Chat (31) and Qwen MT Plus (11). It leads on price. GPT-5 Chat wins on context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceQwen3 235B-A22B Thinking 2507Qwen3 235B-A22B Thinking 2507 $0.95 · GPT-5 Chat $3.44 · Qwen MT Plus $3.69 per 1M tokens (3:1 blend)
- Longest contextGPT-5 ChatGPT-5 Chat 400,000 · Qwen3 235B-A22B Thinking 2507 262,144 · Qwen MT Plus 16,384 tokens
- Widest inputsGPT-5 ChatGPT-5 Chat: Text, Images · Qwen3 235B-A22B Thinking 2507: Text · Qwen MT Plus: Text
- Self-hostingQwen3 235B-A22B Thinking 2507Publishes downloadable weights (Apache 2.0)
| Measure | Weight | GPT-5 Chat | Qwen3 235B-A22B Thinking 2507 | Qwen MT Plus |
|---|---|---|---|---|
| Price | 50% | 23 | 49 | 22 |
| Inputs & features | 30% | 35 | 35 | 0 |
| Context window | 20% | 44 | 37 | 0 |
| Overall | 100% | 31/100 | 42/100 | 11/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT-5 ChatOpenAI | Qwen3 235B-A22B Thinking 2507Alibaba (Qwen) | Qwen MT PlusAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 143.8 | — |
| ECI rank | — | #93 of 165 | — |
| GPQA DiamondGraduate-level science questions | — | 80.1% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 86.7% | — |
| SimpleQA VerifiedShort factual questions | — | 40.4% | — |
| Price per million tokens | |||
| Input | $1.25 | $0.30 (best) | $2.46 |
| Output | $10.00 | $2.90 (best) | $7.37 |
| Cached input | — | — | — |
| Blended (3:1) | $3.44 | $0.95 (best) | $3.69 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 5 providers | Median of 11 providers | Official Alibaba API |
| Limits | |||
| Context window | 400,000 tokens (best) | 262,144 tokens | 16,384 tokens |
| Max output | 128,000 tokens (best) | 81,920 tokens | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | No |
| Tool calling | No | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | OpenApache 2.0 | Proprietary |
| API model ID | — | — | qwen-mt-plus |
| API providers | 5 | 12 (best) | 3 |
| Released | Aug 7, 2025 | Jul 25, 2025 | Jul 24, 2025 |
| Knowledge cutoff | Sep 30, 2024 | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GPT-5 Chat$32.50
- Qwen3 235B-A22B Thinking 2507$8.80
- Qwen MT Plus$39.34
Which should you choose?
Which is better: GPT-5 Chat, Qwen3 235B-A22B Thinking 2507 or Qwen MT Plus?
Qwen3 235B-A22B Thinking 2507 is the better all-round choice, scoring 42/100 against GPT-5 Chat (31) and Qwen MT Plus (11). It leads on price. GPT-5 Chat wins on context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, GPT-5 Chat, Qwen3 235B-A22B Thinking 2507 or Qwen MT Plus?
Qwen3 235B-A22B Thinking 2507 is cheaper at $0.30 input / $2.90 output per million tokens (median across 11 API providers). GPT-5 Chat costs $1.25 input / $10.00 output per million tokens (median across 5 API providers); Qwen MT Plus costs $2.46 input / $7.37 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.95 per million tokens for Qwen3 235B-A22B Thinking 2507 versus $3.44 for GPT-5 Chat (3.6× as much) and $3.69 for Qwen MT Plus (3.9× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GPT-5 Chat has not been scored yet, Qwen3 235B-A22B Thinking 2507 has an ECI of 143.8 and Qwen MT Plus has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5 Chat, Qwen3 235B-A22B Thinking 2507 and Qwen MT Plus yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that GPT-5 Chat and Qwen MT Plus does not support tool calling, which most coding agents need.
Which has the bigger context window?
GPT-5 Chat has the largest context window at 400,000 tokens, against 262,144 for Qwen3 235B-A22B Thinking 2507 and 16,384 for Qwen MT Plus. Maximum output per response: GPT-5 Chat up to 128,000, Qwen3 235B-A22B Thinking 2507 up to 81,920, Qwen MT Plus up to 8,192 tokens.
Which can read images, PDFs, audio or video?
GPT-5 Chat accepts text and images; Qwen3 235B-A22B Thinking 2507 accepts text; Qwen MT Plus accepts text. GPT-5 Chat handles the widest range of inputs.
Are any of these open source?
Qwen3 235B-A22B Thinking 2507 publishes its weights (Apache 2.0) and can be self-hosted; GPT-5 Chat and Qwen MT Plus is proprietary.
Which is newer?
GPT-5 Chat is the newest, released Aug 7, 2025. Qwen3 235B-A22B Thinking 2507 came out Jul 25, 2025; Qwen MT Plus came out Jul 24, 2025. Knowledge cutoff: GPT-5 Chat Sep 30, 2024.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Nov 2, 2026.
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